HiLiftAeroML: High-Fidelity Computational Fluid Dynamics Dataset for High-Lift Aircraft Aerodynamics
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arXiv
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| Format: | Preprint |
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2026
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| author | Ashton, Neil Clark, Adam Heidt, Liam Ivey, Christopher Bose, Sanjeeb Agrawal, Rahul Goc, Konrad Ranade, Rishi Adams, Corey Sharpe, Peter Nidhan, Sheel Akkurt, Semit Leibovici, Daniel Kossaifi, Jean |
| author_facet | Ashton, Neil Clark, Adam Heidt, Liam Ivey, Christopher Bose, Sanjeeb Agrawal, Rahul Goc, Konrad Ranade, Rishi Adams, Corey Sharpe, Peter Nidhan, Sheel Akkurt, Semit Leibovici, Daniel Kossaifi, Jean |
| contents | This paper describes the first-ever open-source high-fidelity CFD dataset of a high-lift aircraft for the purpose of AI surrogate model development. The dataset is composed of 1800 samples, arising from 180 geometry variants and 10 angles of attack for the high-lift NASA Common Research Model (CRM) geometry, used within the AIAA High-Lift Prediction Workshop series. One of the novelties of this dataset is the use of a GPU-accelerated high-fidelity explicit, wall-modeled LES approach for each simulation, using solution-adapted grids between 300M and 500M cells. This ensures the greatest possible accuracy given known challenges in steady-state RANS approaches for these portions of the flight envelope. The entire dataset (geometries, time-averaged volume and surface variables and integral forces) are available, free of charge with a permissive open-source license (CC-BY-4.0). By making this data publicly available, we aim to accelerate the research and development of AI surrogate modeling within the aerospace industry. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_19565 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | HiLiftAeroML: High-Fidelity Computational Fluid Dynamics Dataset for High-Lift Aircraft Aerodynamics Ashton, Neil Clark, Adam Heidt, Liam Ivey, Christopher Bose, Sanjeeb Agrawal, Rahul Goc, Konrad Ranade, Rishi Adams, Corey Sharpe, Peter Nidhan, Sheel Akkurt, Semit Leibovici, Daniel Kossaifi, Jean Fluid Dynamics Machine Learning This paper describes the first-ever open-source high-fidelity CFD dataset of a high-lift aircraft for the purpose of AI surrogate model development. The dataset is composed of 1800 samples, arising from 180 geometry variants and 10 angles of attack for the high-lift NASA Common Research Model (CRM) geometry, used within the AIAA High-Lift Prediction Workshop series. One of the novelties of this dataset is the use of a GPU-accelerated high-fidelity explicit, wall-modeled LES approach for each simulation, using solution-adapted grids between 300M and 500M cells. This ensures the greatest possible accuracy given known challenges in steady-state RANS approaches for these portions of the flight envelope. The entire dataset (geometries, time-averaged volume and surface variables and integral forces) are available, free of charge with a permissive open-source license (CC-BY-4.0). By making this data publicly available, we aim to accelerate the research and development of AI surrogate modeling within the aerospace industry. |
| title | HiLiftAeroML: High-Fidelity Computational Fluid Dynamics Dataset for High-Lift Aircraft Aerodynamics |
| topic | Fluid Dynamics Machine Learning |
| url | https://arxiv.org/abs/2605.19565 |